pdata_sharing <- df %>%
filter(!fund.on.data.history.) %>%
select(.run.number., .step., share.data.,
mean.grants.groups:sum..total.primary.publications..of.groups) %>%
pivot_longer(-c(.run.number., .step., share.data.)) %>%
drop_na()
pdata_sharing %>%
filter(str_detect(name, "gini")) %>%
ggplot(aes(.step., value, colour = share.data.)) +
geom_smooth() +
facet_wrap(vars(name), nrow = 2, scales = "free_y")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
inequality is lower when sharing data
p <- pdata_sharing %>%
filter(str_detect(name, "gini")) %>%
ggplot(aes(.step., value, colour = share.data., group = .run.number.)) +
geom_line() +
facet_wrap(vars(name), nrow = 2)
plotly::ggplotly(p)
# data sharing with funding reward
data_funding <- df %>%
filter(share.data.) %>%
select(.run.number., .step., fund.on.data.history.,
mean.grants.groups:sum..total.primary.publications..of.groups) %>%
pivot_longer(-c(.run.number., .step., fund.on.data.history.)) %>%
drop_na()
data_funding %>%
filter(str_detect(name, "gini")) %>%
ggplot(aes(.step., value, colour = fund.on.data.history.)) +
geom_smooth() +
facet_wrap(vars(name), nrow = 2, scales = "free_y")
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
p <- data_funding %>%
filter(str_detect(name, "gini")) %>%
ggplot(aes(.step., value, colour = fund.on.data.history., group = .run.number.)) +
geom_line() +
facet_wrap(vars(name), nrow = 2)
plotly::ggplotly(p)